from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   42






class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ):
    @register_to_config
    def __init__(	self			,			lowerCAmelCase = 65536			,			lowerCAmelCase = None			,			lowerCAmelCase = 2			,			lowerCAmelCase = 2			,			lowerCAmelCase = 0			,			lowerCAmelCase = "fourier"			,			lowerCAmelCase = True			,			lowerCAmelCase = False			,			lowerCAmelCase = 0.0			,			lowerCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,			lowerCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,			lowerCAmelCase = "UNetMidBlock1D"			,			lowerCAmelCase = None			,			lowerCAmelCase = (32, 32, 64)			,			lowerCAmelCase = None			,			lowerCAmelCase = 8			,			lowerCAmelCase = 1			,			lowerCAmelCase = False			,			)							->       str:
     super().__init__()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   sample_size

     # time
     if time_embedding_type == "fourier":
      SCREAMING_SNAKE_CASE__: List[Any]=   GaussianFourierProjection(
          embedding_size=8			,			set_W_to_weight=lowerCAmelCase			,			log=lowerCAmelCase			,			flip_sin_to_cos=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: int=   2 * block_out_channels[0]
     elif time_embedding_type == "positional":
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   Timesteps(
          block_out_channels[0]			,			flip_sin_to_cos=lowerCAmelCase			,			downscale_freq_shift=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   block_out_channels[0]

     if use_timestep_embedding:
      SCREAMING_SNAKE_CASE__: Optional[int]=   block_out_channels[0] * 4
      SCREAMING_SNAKE_CASE__: Tuple=   TimestepEmbedding(
          in_channels=lowerCAmelCase			,			time_embed_dim=lowerCAmelCase			,			act_fn=lowerCAmelCase			,			out_dim=block_out_channels[0]			,			)

     SCREAMING_SNAKE_CASE__: Any=   nn.ModuleList([] )
     SCREAMING_SNAKE_CASE__: int=   None
     SCREAMING_SNAKE_CASE__: Optional[Any]=   nn.ModuleList([] )
     SCREAMING_SNAKE_CASE__: List[str]=   None

     # down
     SCREAMING_SNAKE_CASE__: Optional[Any]=   in_channels
     for i, down_block_type in enumerate(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Dict=   output_channel
      SCREAMING_SNAKE_CASE__: str=   block_out_channels[i]

      if i == 0:
       input_channel += extra_in_channels

      SCREAMING_SNAKE_CASE__: Any=   i == len(lowerCAmelCase ) - 1

      SCREAMING_SNAKE_CASE__: Tuple=   get_down_block(
          lowerCAmelCase			,			num_layers=lowerCAmelCase			,			in_channels=lowerCAmelCase			,			out_channels=lowerCAmelCase			,			temb_channels=block_out_channels[0]			,			add_downsample=not is_final_block or downsample_each_block			,			)
      self.down_blocks.append(lowerCAmelCase )

     # mid
     SCREAMING_SNAKE_CASE__: Optional[int]=   get_mid_block(
         lowerCAmelCase			,			in_channels=block_out_channels[-1]			,			mid_channels=block_out_channels[-1]			,			out_channels=block_out_channels[-1]			,			embed_dim=block_out_channels[0]			,			num_layers=lowerCAmelCase			,			add_downsample=lowerCAmelCase			,			)

     # up
     SCREAMING_SNAKE_CASE__: Any=   list(reversed(lowerCAmelCase ) )
     SCREAMING_SNAKE_CASE__: List[str]=   reversed_block_out_channels[0]
     if out_block_type is None:
      SCREAMING_SNAKE_CASE__: str=   out_channels
     else:
      SCREAMING_SNAKE_CASE__: str=   block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Any=   output_channel
      SCREAMING_SNAKE_CASE__: Any=   (
          reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase ) - 1 else final_upsample_channels
      )

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   i == len(lowerCAmelCase ) - 1

      SCREAMING_SNAKE_CASE__: List[str]=   get_up_block(
          lowerCAmelCase			,			num_layers=lowerCAmelCase			,			in_channels=lowerCAmelCase			,			out_channels=lowerCAmelCase			,			temb_channels=block_out_channels[0]			,			add_upsample=not is_final_block			,			)
      self.up_blocks.append(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   output_channel

     # out
     SCREAMING_SNAKE_CASE__: Any=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,			32 )
     SCREAMING_SNAKE_CASE__: int=   get_out_block(
         out_block_type=lowerCAmelCase			,			num_groups_out=lowerCAmelCase			,			embed_dim=block_out_channels[0]			,			out_channels=lowerCAmelCase			,			act_fn=lowerCAmelCase			,			fc_dim=block_out_channels[-1] // 4			,			)



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = True			,			)							->       Union[UNetaDOutput, Tuple]:
     SCREAMING_SNAKE_CASE__: Dict=   timestep
     if not torch.is_tensor(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Dict=   torch.tensor([timesteps]			,			dtype=torch.long			,			device=sample.device )
     elif torch.is_tensor(lowerCAmelCase ) and len(timesteps.shape ) == 0:
      SCREAMING_SNAKE_CASE__: Optional[int]=   timesteps[None].to(sample.device )

     SCREAMING_SNAKE_CASE__: Dict=   self.time_proj(lowerCAmelCase )
     if self.config.use_timestep_embedding:
      SCREAMING_SNAKE_CASE__: Any=   self.time_mlp(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   timestep_embed[..., None]
      SCREAMING_SNAKE_CASE__: Dict=   timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
      SCREAMING_SNAKE_CASE__: Tuple=   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

     # 2. down
     SCREAMING_SNAKE_CASE__: Optional[Any]=   ()
     for downsample_block in self.down_blocks:
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   downsample_block(hidden_states=lowerCAmelCase			,			temb=lowerCAmelCase )
      down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
      SCREAMING_SNAKE_CASE__: List[Any]=   self.mid_block(lowerCAmelCase			,			lowerCAmelCase )

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   down_block_res_samples[-1:]
      SCREAMING_SNAKE_CASE__: Optional[Any]=   down_block_res_samples[:-1]
      SCREAMING_SNAKE_CASE__: int=   upsample_block(lowerCAmelCase			,			res_hidden_states_tuple=lowerCAmelCase			,			temb=lowerCAmelCase )

     # 5. post-process
     if self.out_block:
      SCREAMING_SNAKE_CASE__: Any=   self.out_block(lowerCAmelCase			,			lowerCAmelCase )

     if not return_dict:
      return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase )


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
